What do we talk about when we talk about “Data Exploration”?

There is an old “adage” in the InfoVis / Visual Analytics community I have heard a zillion times: “visualization is needed/useful when people don’t have a specific question in mind“. For many years I have taken this as “the verb”. Then, over time, as I have grown more experienced, I have started questioning the whole concept: why would someone look at a given data set if he or she has no specific goal or question in mind? It does not make sense.

This is an aspect of visualization that has puzzled me for a long time. An interesting conundrum I believe it is still largely unsolved. One of those things many say, but nobody really seems to have grasped in full depth.

Here is my humble attempt at putting some order into this matter. Let’s start with definitions:

The definition introduces a couple of very important features: familiarity (“through an unfamiliar area“) and learning (“in order to learn about it“). If we take this definition as main guidance, we can say that data visualization is particularly helpful when we use it to look into some unfamiliar data to learn more about something.

I suspect there are (at least) three main situations in which this can happens.

  1. Need to familiarize with a new data set (“how does it look like?“). Anyone who dabbles with data a bit goes through this: you receive or find a new data set and the first thing you need to do is to figure out what information it contains. How many fields? What type of fields? What is their meaning? Are there any missing values? Is there anything I don’t actually understand? How are the values distributed? Is there any temporal or geographical information? Are we actually in presence of some kind of network or relation structure? Etc. One crucial, and often overlooked, aspect of this activity is “data semantics”. I personally find that understanding the meaning of the various fields and the values they contain is a such a crucial and hard activity at the beginning. An activity that often requires many many back-and-forth discussions and clarifications with domain experts and data collectors.
  2. Hunting for “something” interesting (“is there anything interesting here?“). I suspect this is what people mostly really mean when they talk about “data exploration“: the feeling that something interesting may be hidden there and that some exploratory work is needed to figure it out. But … When does this actually happen? What kind of real-world activities are characterized by this desire of finding “something”? I am not sure I have an all-encompassing answer to that, but I am familiar with at least two examples: data journalism and quantified self. In data journalism it is very common to first get your hands on some potentially “juicy” data set and then try to figure out what interesting stories may hide there (Panama Papers, Clinton’s Emails, Etc.). I have observed this in our collaboration with ProPublica when hunting for stories about how people review doctors in Yelp. In quantified self you often want to look at your data to see if you can detect anything unexpected. I have experienced the same when looking at personal data I have collected about my deep work habits (or lack thereof). Sometimes we know there must be something interesting in a given data set, and visualization guides us in the formulation of unexpressed questions. The interesting aspect of this activity is that the outcome is often more (and better) questions, not answers.
  3. Going off on a tangent (“oh … this may be interesting too!“). There is one last, subtler, kind of data exploration. You start with a specific question in mind but, as you go about it, you find something interesting that triggers an additional question you had not anticipated. This is the power of visual data analysis, it forces you to notice something new and you have to follow the path. This happens to me all the time (and I hope it’s just not a sign of my ADD). Some of these are useless diversions. Some of them actually lead to some pretty unique gems!

These three modalities can of course overlap a lot. I am also sure there are other situations we can describe as data exploration which I am not covering here (in case you have some suggestions please let me know!).

I want to conclude by saying that this is an incredibly under-explored area of data visualization. More advances are needed at least in two directions.

  • First, we need to much better understand data exploration as a process and, if possible, create models able to describe it in useful abstract terms. In visualization research we often refer to Card and Pirolli’s “Sensemaking Loop” to describe this kind of open-ended and incremental activity but for some reason every time I try to use it, it does not seem to describe what I actually observe in practice (this deserve its own post).
  • Second, we need to develop more methods, techniques and tools to support interactive data exploration. I bet there are lots of “latent needs” waiting to be discovered out there. This is another area where I believe we, visualization researchers have surprisingly made little progress. We have built a lot of narrow solutions that work for 3-5 people but very few general purpose methods and techniques. We need more of that (this also deserves its own post)!
  • Third, we need to find ways to teach exploratory data analysis systematically to others in ways that make the process as effective as possible. I am appalled at how little guidance and material there is out there on teaching people how to do the actual analysis work. Statisticians are fixated with confirmatory analysis and regard exploration as a second-class citizen. Visualization researchers are too busy building stuff and have done too little to teach others how to do the actual ground work. This is a problem we need to solve. It’s for this reason that next semester I will be teaching a new course with this specific purpose. Stay tuned.

That’s all I had to say about Data Exploration.

And you? What is your take? What is data exploration for you? And how can we improve it?

Take care.

11 (Papers + Talks) Highlights from IEEE VIS’16


Hey, it took me a while to create this list! But better later than never. Here is my personal list of 11 highlight from the IEEE VIS’16 Conference.

If you did not have a chance to attend the conference you can start from here and then look into the following links:


Surprise! Bayesian Weighting for De-Biasing Thematic Maps.
Michael Correll, Jeffrey Heer.

Did you ever stumble into one of those choropleth maps in which the distribution of a given quantity is shown, (say, number of cars from a given manufacturer) but the only signal you can see is actually population density? This is the kind of problem Surprise! addresses. It deals with situations in which the quantity one wants to depict is confounded by another variable. To solve this problem Surprise! uses an underlying Bayesian model of how the quantity should be distributed and visualizes deviations from the model rather than quantity (hence the name Surprise!).

I think this is a brilliant idea which addresses a super common problem. I have seen people stumble into this problem countless times and I am glad we finally have a paper that explains the phenomenon and proposes a solution. The only issue is that visualizing surprise is not as natural as visualizing the actually quantity; which is normally what people would expect. One open challenge then is how to communicate both values at the same time.

Vega-Lite: A Grammar of Interactive Graphics.
Arvind Satyanarayan, Dominik Moritz, Kanit “Ham” Wongsuphasawat, Jeffrey Heer.

The IDL team has done over the years and astounding job at developing an ecosystem of frameworks and tools to make the development of advanced visualizations easier and faster. Vega-Lite builds on top of Vega, which they presented last year, and proposes a much simpler language and extremely powerful functions to generate interactive graphics (with linked views, selections, filters, etc.). Arvind and Dominik gave a live demo and I have to say I am really impressed. While most existing frameworks focus on the representation part of visualization, this one focuses on interaction and as such it covers a really big gap. I am curious to see what people will manage to build using Vega-Lite. If you built some interactive visualizations in the past you certainly know that the interaction part is by far the hardest and messiest one. Vega-Lite seems to make it much simpler and straightforward than it used to be. I am looking forward to trying it out!

PROACT: Iterative Design of a Patient-Centered Visualization for Effective Prostate Cancer Health Risk Communication.
Anzu Hakone, Lane Harrison, Alvitta Ottley, Nathan Winters, Caitlin Guthiel, Paul KJ Han, Remco Chang.

PROACT is a simple visualization dashboard that helps patients with prostate cancer understand their disease and make informed decisions about choosing between a conservative solution or surgery. The paper does a great job at describing the context and the challenges associated with such a delicate kind of situation and how visualization systems can be used by doctors and patients to enhance communication.

I consider this paper super relevant. While if you look into the images you won’t be impressed by fancy colorful views and interactions, the system has been demonstrated to be really effective in a very important and critical setting. It also raises awareness about issues we rarely discuss in visualization; especially how to deal with emotions and how to design systems that inform while being careful with the impact such knowledge may have on the viewers.

TextTile: An Interactive Visualization Tool for Seamless Exploratory Analysis of Structured Data and Unstructured Text.
Cristian Felix, Anshul Pandey, Enrico Bertini.

This is the latest product coming out of my lab. I plan to write a separate blog post on it later on. TextTile stems from multiple interactions we had with journalists and data analysts who need to look into data sets containing textual data together with tabular data (e.g., product reviews and surveys). In TextTile we propose a model that describes systematically how one can interactively query data starting from text and reflecting the results on the data table and vice-versa. The tool realizes this model in an interactive visual user interface with a mechanism similar to what is found in Tableau: the user creates queries and plots by dragging data fields to a predefined set of operations. I suggest you to try it on your own! You can find a demo here: http://texttile.io/.

Evaluating the Impact of Binning 2D Scalar Fields.
Lace Padilla, P. Samuel Quinan, Miriah Meyer, and Sarah H. Creem-Regehr.

I chose to include this paper because I found its message extremely inspiring. In visualization research we often cite a principle (proposed by Jock MacKinlay) called the “expressiveness principle“. The principle  states that “a visual encoding should express all of the relationships in the data, and only the relationships in the data“. This paper shows that this principle may actually not always hold. The paper describes experiments in which performance improves when a continuous value is presented with discrete color steps rather than continuous; a solution that breaks the expressiveness principle.  This may seem a minor detail but I believe it demonstrates a much bigger idea: there is lots of conventional wisdom ready to be debunked and it is up to us to hunt for this kind of research. Every single scientific endeavor is a loop of construction and destruction of past theories and idea. This paper is a great example of the destruction part of the cycle. We need more papers like this one!

VizItCards: A Card-Based Toolkit for Infovis Design Education.
Shiqing He and Eytan Adar.

What a lovely lovely project! If you have ever tried to teach visualization you know how hard it is. Students just don’t get it if you give lectures and lots of theory. Visualization needs to be learned by doing. But organizing a course on doing in a systematic way is hard. Damn hard! Shiqing and Eytan have done an amazing job at making this process systematic and easy to adopt. They developed a toolkit and a set of cards instructors can use to guide students during a series of design workshops. One aspect I like a lot, other than the cards idea, is that many exercises have been ideated starting from an existing data visualization project and “retrofitted” to their original “amorphous” status of having a bunch of data and a vague goal. This is what the students are shown at the beginning and at the end of the process they can compare their results with the results developed in the original project. You can find the toolkit here: http://vizitcards.cond.org/supp/index.html. I am planning to adopt some of it myself next time I’ll teach my course (too late for this semester).

Colorgorical: Creating discriminable and preferable color palettes for information visualization.
Connor C. Gramazio, David H. Laidlaw, Karen B. Schloss.

Creating categorical color palettes is a hard task and if you want to do it manually it’s even harder. Colorgorical is a new color selection tool that enables you to build new categorical color palettes using a lot of useful and interesting parameters, including: perceptual difference, name difference, pair preference, and name uniqueness. An internal algorithm tries to optimize all the desired parameters and generates a new color palette for you. You can also add starting colors to make sure some colors you want to have are actually present in the final color palette. I strongly suggest you to play with it! They have a nice web site explaining all the parameters and a simple interface to generate new palettes.


An Empire Built On Sand: Reexamining What We Think We Know About Visualization.
Robert Kosara.

Robert’s talk was more of a performance than a talk. I really really enjoyed it. His talk at BELIV was all focused on the idea that we in vis regard some ideas as truth and keep repeating them even if evidence for them is actually weak or nonexistent. Robert kept repeating, in a wonderfully coordinated sequence, “how do we know that?” … “how do we know that?” … “how do we know that?“. I loved it. Too bad the talk was not recorded. But you can find the accompanying paper here. Kudos to Robert for assuming the role of contrarian at vis. We really need people like him who do not hold back, speak with candor, and are ready to yell the “the emperor has no clothes”.

We Should Never Stop BELIVing: Reflections on 10 Years of Workshops on the Esoteric Art of Evaluating Information Visualization.
Enrico Bertini.

Here is another one from yours truly. I started the BELIV workshop on evaluation in vis in 2006 with Giuseppe Santucci (my PhD advisor) and Catherine Plaisant and the organizers kindly asked me to give a keynote for the 10 years anniversary. If you click on the URL above you can watch the entire talk. I tried to be funny and also to give a sense of how much progress we have made and what may come next. Evaluation in visualization is a continuously evolving endeavor and there is much to learn and perfect. The vis community has been receptive to new ideas on how to conduct empirical research and I predict we will see a lot of innovation in coming years. Let me know what you think if you watch the video!

Capstone Talk: The three laws of communication.
Jean-luc Doumont.

Wow! I had absolutely no idea who Jean-Luc was before I entered the room and started listening to his talk. This is by far one of the best capstone talks I have ever attended at VIS, if not the best. Jean-Luc gave a talk on how to convey messages effectively and organized it around a number of principles he developed through the years of his activity training people on effective communication. This guy know what he is talking about. His body language, the way he expresses his thoughts, the quality and density of information in what he says, the style of his slides, etc., everything is great. His work can inform any professional who needs to communicate information better, being it visual or verbal. He has a fantastic book which looks very much like Tufte’s but more on general communication. If you have never heard of him take a look at his work, he is amazing … and super fun!

Communicating Methods, Results, and Intentions in Empirical Research.
Jessica Hullman.

Jessica is doing some of the most interesting type of work in visualization. Her blend of core statistical concept and visualization is very much needed and one of the most interesting recent trend in vis: how to use vis to communicate statistics better and, at the same time, how to use statistics to do better vis research. In her short talk Jessica raised a number of important points on how we communicate research, not only to others but also to ourselves, and how we can introduce practices that may reduce the chances we are fooling ourselves. The world of experimental research and statistics is changing very fast and we are witnessing a wave of great self-criticism and reform. While this is true for science in general, the world of visualization research is also very receptive to what is happening and Jessica is one of the few vis people who is helping us make sense of it.

That’s all folks! I hope you’ll find these projects inspiring!

InfoVis Course Diary: Basic Charts Need to Be Learned First

In the third week of my course I introduce fundamental charts. These are charts that are super common and most people are familiar with: bar charts, histograms, line charts, scatter plots, heat maps, etc. This is a major departure from the textbook I use, which introduces methods to visualize tabular data only in later chapters.

Why do I start with basic charts?

It’s pedagogically better to start with basic rather than advanced charts. I truly believe one must first learn how to use basic charts properly before moving on to other more exotic territories. Within their constrained space, there is a lot to learn and there are infinite variations and tweaks one can apply.

These charts, in their most basic format, cover all possible combinations of two attributes, thus giving students a manageable and yet powerful mental model to think about how pairs of attributes can be combined: a scatter plot is made of 2 quantitative attributes; a bar chart is made of 1 categorical and 1 quantitative attribute; a line chart is also made of 2 quantitative attributes but one is special as it represents time;  a heat map is a combination of two categorical attributes and their frequencies, etc.

These charts are also infinitely “tweakable” while retaining simplicity. For instance, what happens to these charts when you need to map an additional 3rd attribute? In scatter plots you can use color and/or size. In bar charts you can use stacked or grouped bars. In line charts you can add multiple lines.

Take this scatter plot below in which I am plotting data from the USDA food nutrients database (each dot is a food, axes are: amounts of mono-saturated and poly-saturated fats).

Scatter plots are infinitely “tweakable”. Here we progressively encode 2, 3 and 4 attributes with position, color hue and size.

In its most basic format it encodes two quantitative attributes (the amount of two type of fats). The next one encodes a third categorical attribute (food type) using color hue. And the final one encodes a fourth attribute (amount of water) with size. That’s a very gentle and yet solid way of introducing fundamental visualization concepts. In class I am actually cycling through many of these examples, starting from the most basic chart and asking students to accommodate more data or needs.

With these basic charts one can also start introducing examples of problematic design solutions. For instance, it’s easy to talk about the “truncated axis” and the “dual axis” problems.

See, for instance, the infamous Planned Parenthood hearing charts that made the news last year.

Misleading chart in which the dual-axis method has been used to give a false impression about the data.

Another aspect is that basic charts are an amazing toolbox for the visualization designers because the very large majority of existing problems can be solved with them. It’s very rare to find a visualization problem that cannot be solved, at least in a first approximation, with these basic designs. Plus, whenever needed, you can always try to apply little modifications to conform them to your needs. I really believe there are way more interesting designs one can generate by tweaking these basic charts than trying to come up with something entirely new.

Finally, basic charts are the most familiar ones. If in your project familiarity is an important aspect you don’t want people to spend time figuring out how to decode your charts. This is an aspect that is often overlooked in visualization. When presented with a new visualization the first thing the reader/user needs to figure out is “how do I actually read this?“.

Some questions from the students …

Before I conclude I want to briefly touch upon a few recurring questions students asked in their reading response exercise (I’m paraphrasing).

Q: “Are pie charts really so bad? I like them!

Students are always puzzled when they see that pie charts are so heavily criticized by some people. I am personally not interested at all about the pie charts debate. I am not because I do not think it’s really consequential. In any case what I tell my students is that they should never use rules blindly. So pie charts are a good example to exercise their own good judgment. When are they appropriate? When are they not? This is what matters the most to me. I am myself not a big fan of pie charts but I do not believe they are evil, and I do believe there situations in which it may be reasonable to use them. Robert Kosara has published a good number of interesting blogs posts and papers on the topic.

Q: “How do we deal with people’s subjective preference for some charts?

That’s actually a big one. Students at the beginning of the course are still puzzled by the idea that some charts may be objectively better than others in some contexts and for some tasks. Because of that, they feel lost when they think that some people reading their charts may actually find them not exactly of their favorite “taste”.

This is too long a subject to be developed fully here. But the main thing to learn is that charts need to be effective before being “pleasurable”. Aesthetics plays a role of course but it cannot subsume effectiveness. Therefore one needs to learn what is effective and what is not and then find a way to “inject” the right aesthetic sense within these constraints.

The best data visualizations out there (and the best designers by the way) are those that are great at finding this fine balance.

But there is more to say on the topic. A very important principle, dear to user experience designers, is that good design is about giving people what they need, not necessarily what they tell you they need. It’s your responsibility as a designer and as an engineer to figure out what is the best solution for your audience, you can’t rely exclusively on what they tell you they want or need.

Q: “How do we deal with large data sets? These charts do not scale!

Correct. Many of the basic charts do not scale to large data sets or large number of values. But this is exactly the point! By realizing what the limitations of basic charts are, one is forced to think about what the alternatives are. It’s a very useful step from the pedagogical point of view.

But even before moving to more exotic solutions, students first need to figure out how scalable basic charts really are. A very consistent trend I found in students is that they are afraid of making charts small and, because of that, they largely underestimate how scalable they really are!

As Tufte’s work on “sparklines” demonstrates, charts can be incredibly small and still convey a lot of information. So, while there are cases in which standard charts may actually not be sufficiently scalable, I consider it crucial to first show students how to make charts small. Very small.

That’s all for now. Hope this helps!

InfoVis Course Diary: Update on Class Flipping

Flipping ClassroomMy adventure on flipping my InfoVis class is moving on. Here is a brief update on what is happening in class and a few small lessons learned during the first four weeks of the course.

Please keep in mind this is work in progress and pretty much a brain dump. I may in the future find that some of these ideas are not as brilliant as they look right now.

Students need more time than you think to develop their exercises in class. During the first three weeks I have consistently underestimated the time it takes to students to develop solutions for their exercises. My class lasts two hours an a half and it is barely enough time to develop an exercise fully. In the first two weeks I planned to have time for three different activities, now I know I have to basically focus on only one. Yesterday, I assigned a data analysis and presentation exercise (to be developed with Tableau) based on a real-world data set and the two hours an a half have been barely enough.

The exercises need to be broken down into smaller (timed) steps. It is very hard for students to mentally “digest” a complex exercise as a single unit. A much better strategy is to break the exercise down into smaller steps that lead to the final solution. A big advantage of this strategy is that at the end of each step I can comment on what the students have just done and how it connects to higher level concepts. I try to enable this generalization by providing comments and also asking a few direct questions. This seems to work really well. For instance, I broke the data analysis and presentation exercise down to: (1) familiarize with the data and its meaning; (2) generate analytical questions; (3) do data analysis in Tableau; (4) collect the results and organize them in a narrative; (5) write a final document. At each of these steps there are plenty of comment one can insert. I have also found that timing these steps and making the timing explicit and visible to the students upfront helps organizing the work and make sure that groups are mostly in sync.

Individual or group work? So far I have experimented exclusively with group work. None of the exercises I have assigned in class require students to work on something individually. My biggest fear is that group dynamics may actually play against students who are particularly shy or simply do not feel like doing the required work for that day. This is an aspect I still need to perfect. What I found so far is that simply walking around and offering help seems to keep the students alert and active. I also try to actively spot students who seem to be on the verge of  disengagement and encourage them to participate more. Yet, at the same time, in a few instances I found that my interventions was not needed and that students have a very clever and natural way to alternate between solitary reflection and sharing with the group. If anything, what I am learning is to trust the process and my students much more and give them freedom to organize their work as they wish.

Am I useful/needed after all? Let’s admit it. Standing in class and walking around without uttering a word does not feel as good as doing the somewhat self-righteous work of giving a lecture. I am no longer the main deal. Students don’t look at me, they have their heads buried in their problems. Is that bad? No. It’s actually good. But I keep asking myself, as the class unfolds, what is the best way for me to be useful here? It feels like swimming in a new kind ocean. My sense is that I still have a lot to learn. For now what I am doing is the following: (1) I make sure to be available for everyone and to devote the same amount of time to every group; (2) I check that no one is having major problems and/or being overshadowed by other students within a group; (3) I follow in real time what students are producing (see note on GDoc below) and make sure to stop at regular intervals to comment on what they are doing right/wrong and to channel their thinking towards elements of the exercise that generalize to other cases. An important note: I am not allowing myself to check my emails or use my phone, it’s so easy to be sucked in and lose focus. I am now some kind of coach and need to be totally focused on the class dynamics.

Room space and arrangement is crucial. In my room there is not enough space and we don’t have tables, only chairs with folding tables (which by the way I always hated as a student also). This is definitely not optimal. So, if you are reading this and are making plans for your class, try to get a spacious room with tables! In my case I literally force students to turn their chairs in a way that they are arranged in circles. I truly believe this is crucial to make sure students are actually working together. I actually tell them they should do it because I am worried they would develop neck pain if they do not turn their chairs, but it’s more because I want them to face each other :)

How about theory and principles (a.k.a. do they get it?)? I was explaining to a colleague the other day my flipping experiment and her concern was: “but are they getting the theory right?“. Good point. Is all of this active work going to translate into higher-level knowledge students will be able to internalize and transfer to other situations? The honest answer is: I don’t know. Yet, a few reflections are due here. First, there’s tons of education research in support of active learning. Students to get the higher level knowledge that is needed. Second, my experience is that students won’t get it when you lecture them either! This is the real reason why I am doing this. My hope is that applying the principles in practical exercises is going to cement the knowledge they acquire while reading and watching the video lectures. I am planning for some additional assessment later on in the course. Hopefully the results will be positive.

Real-time exercise development in Google Docs works like magic. I talked about how I use Google Docs in class before. I totally love it. Let me describe this again. Every single exercise I assign in class requires producing material that goes in a GDocs file (one for each group). I create a folder and ask students to put their files there. Since GDoc files update in real time, I can literally follow what each group is doing without interfering too much with their work. This is such a powerful tool! It feels like magic. Watching how the exercise develops enables me to figure out what is happening and intervene when necessary. Not just that, I can also use examples from one group, to show to all the others group positive or negative aspects of a given solution. And this is priceless.

Tableau is great for teaching. This week the exercise we developed in class requires the students to develop a solution with Tableau. While Tableau is not necessarily super intuitive all the time, it has the big advantage that moving first steps is extremely easy. Students can very quickly produce initial charts. When then they get stuck with something, I explain how to solve it, and some little learning happens. Another practical advantage of Tableau is that students feel like learning it is a good investment of their time: Tableau is very popular and highly requested in jobs applications. Finally, Tableau teaches students to think in terms of mapping data attributes to visual channels, which is the foundation of visual encoding; by far the most important piece of knowledge taught in a visualization course.

Ok … That’s all for now. I really hope you’ll find this useful. Your feedback and questions are very welcome!

InfoVis Course Diary: Dabbling with Data Abstraction

Chapter on Data AbstractionIn the second week of my course we start directly with the “data abstraction” chapter taken from Tamara’s book, which is the official textbook in my course.

Data abstraction is mostly about describing data in a way that is instrumental to visualization design. That is, by detecting certain structures and information within them, you can think ahead about how to navigate the visualization design space. This is where you learn, among other things, the extremely valuable concept of attribute type: categorical, ordinal and quantitative.

Tamara did an excellent job at creating a consistent catalog of data abstractions; which covers an extremely wide set of cases and situations. Here is an excerpt from the data abstraction summary you can find in the book chapter (side note: I absolutely love the way the book provides diagrams summarizing the content at the beginning of each chapter):

Data Abstraction

Summary of “Data Abstraction” chapter from “Visualization Analysis and Design” book.

That said, there are a few things my students and I are always struggling with. Here is an account of the problems and possible solutions.

  1. Some data abstractions are way to complex and cover rare cases. It is clear that Tamara tried to be as complete as possible and, as such, her effort is highly laudable. The need for completeness however creates a tension with simplicity. As you can see in the diagram, some abstractions are very familiar: table, network, tree, etc. But then things get way muddier with grids, spatial fields, geometry, clusters and sets. I have been through this many times and invariably when we get to this chapter my students are confused. I must confess I am confused too. Here are examples of questions I received this semester: “I don’t really understand the concept of field and geometry, especially the difference between them” or “While looking at the continuous field example about sea temperature of locations on the
    planet, it somehow seems like to be geometry dataset type?“. What is the solution? I don’t know. As I said, I like the completeness and the consistent approach, but I am also concerned with how much students will be able to retain. Maybe we can create a “data abstraction light”? My data abstraction light would be something along these lines. There are two data set types: tables and networks. Each of these contain attributes of three possible types: categorical, ordinal and quantitative. Some of these may represent time and/or geography. Too simplistic?
  2. Is data abstraction a matter of “describing” or “designing” data structures? Whenever I teach data abstraction, I feel like there is one important part I should be teaching and it’s not developed enough: the art of “sculpting” your data so that it has the “shape” it is need to solve the problem you want to solve. To be fair, Tamara does talk about this, but I believe this part is not developed/structured enough. While data abstraction helps you describe what kind of information your data contain (description), you also need to figure out how your data can and should be molded to get to the desired solution (design). This is a crucial vis design activity and it’s never acknowledged enough. Let me state it in a different way: your role as a visualization designer is not only to find how to represent the data you have, but also what to represent in the first place. This is a huge aspect of visualization design which is very often overlooked! Now … In how many ways can data be manipulated? And which of these ways do we need to teach? Above all, I believe that anything resembling an SQL query (or Pivot Table in Excel-ese) is a fundamental step (this is why I believe Tableau is so successful: ultimately it’s a database query system). So, fundamental operations in a table are: selecting attributes, filtering rows in a principled manner and aggregating them according to aggregate operations. In a way, every chart can be described as an SQL query over a data set. Other fundamental operations are those that transform a data set or attribute from one type to another: from table to network, from quantitative to categorical, from place names to their coordinates, etc. I believe this is so crucial because there is never a unique way of looking at a given data set. In a way rather than calling this step “data abstraction” I would even call it “data interpretation” Or … “data design“? Or … “data sculpting“? For instance, in class I often use the Aid Data data set. It’s a fantastic data set recording information about financial disbursements between countries for aid purposes over time. The data is stored as a table/spreadsheet and contain, among other fields: origin, destination, time, amount, purpose. Now, if I select origin, transform it into spatial coordinates, and aggregate over amount, I can create a nice bubble chart map of donors. But if I select origin, destination and amount, I can create a weighted node-link diagram. Whereas, If I select  purpose and aggregate by amount, I can create a bar chart of how disbursements distribute across purposes. You see how crucial this is?
  3. Not enough emphasis on the relationship between analytical questions and “data shapes”. This is somewhat related to my last observation. Data abstraction and transformation do not happen in a vacuum, they are instrumental to achieving a data analysis and presentation goal. But data analysis and presentation presuppose looking for answers to a series of questions. Ultimately, this is what happens in practice: finding the right “shape” for your data is guided by your desire to pursue some questions and goals. This is easier said than done. One caveat in visualization is that not all questions are perfectly laid out in front of us. Sometime we “discover” new questions as we proceed in our analysis. In any case, it is true that virtually every single visual representation has one or more possible questions attached, that is, questions that can be answered by looking at it (and new questions that cannot be solved by looking at it by the way). I now feel that this tight relationship between questions, data sculpting and representation needs to be highlighted and trained. I have done a bit of this in class already, through exercises, and my students seem to have learned a lot. One student at the end of the class told me: “Prof. I loved this exercise today!“. On a side note, this is why I am so happy I no longer need to give lectures in class. I can afford teaching students concepts through practice. They end up developing a sense of what these concepts really mean, not only intellectually, but also in practical (more internalized) ways.

That’s all for now. My next post will be on the third module I teach, which is “fundamental variations of charts”. Let me know what you think! This is very much work in progress!

Thanks for reading.

InfoVis Course Diary: Flipping My Class and Other Innovations

flipped-classroomLast week I started a new edition of “Information Visualization“, the course I give at NYU Tandon, my university. With this blog post I am starting a new series, similar to the one I did a couple of years back, in which I write about the course and ideas that originate from my experience while teaching it.

In this first post I want to talk about new ideas I am implementing, in the hope this will be inspiring and helpful to other instructors like me. Last summer, I spent quite some time reviewing education research and reading books on the topic (while preparing my NSF CAREER), and I felt I really needed to change a few things in the way I am teaching. Here are some of the more most prominent ones.

Flipped classroom model. For the first time this semester I will be using a flipped classroom model. In this model students read assigned material and watch my recorded lectures (which I had recorded in previous semesters) at home. Time in class is entirely spent on feedback, discussions and exercises.

Why do I do that? First, because throughout the years of teaching I learned that I am the most valuable when I am giving feedback to my students, not when I am lecturing. The get the best out of me when I show them what is a better way to solve a given problem. Second, because lectures allow (encourage?) students to be totally passive and to disengage (a weird kind of luxury given how much it costs to be an NYU student). When this is then compounded with how pervasive digital distractions are in today’s classroom, this is a perfect recipe for disaster. Finally, I am just bored of spending two and a half hours talking and seeing everyone progressively “fade away” (together with my voice).

Last week I gave my first “flipped class” and I believe it went very well. There are a few things I’ll need to fix, but overall it seems to work. It’s work in progress and I expect to learn a lot.

Each week I ask my students to answer a few questions about the readings/videos I assigned them to read/watched and I use their answers to provide feedback to everyone at the beginning of each class. Three benefits: (1) I now have time to review all students’ responses before going in class (as opposed to preparing the lecture); (2) I can cluster them into major themes/issues and use this information to improve the course; and (3) since I discuss this in front of the class, everyone can hear what I have to say and still reacts and benefit from it. Next week, I’ll assign the first hands-on exercise in class. I am hopeful this also will work just right.

Decoupling grading and assessment. This is another no-brainer I figured out only a few months ago: assessment and grading do not need to coincide. The main goal of assessment is not to assign a grade to each student, but: (1) to inform the instructor about how much and what students are learning and (2) to inform the students about what aspects of the assigned material they still need to work on. When you see it this way, it transforms completely the way you think about assignments and exercises. I am relieved by the burden of turning everything into a grade and at the same time I now see this as my little tool I use to decide what to talk about in class and tweak it.

In my new model, assessment and grading are decoupled. I use assessment to guide myself and the students, and grading, through a test later on to give a final grade to a given set of modules.

Of course, I still need to check that students are actually doing the work I ask them to do at home. For this reason all my assignments are mandatory and graded only on a pass/fail scale. My students know that their final grade will be affected only slightly by these assignments but at the same time they know they must submit and take it seriously. In short, it’s hard to fail and your grade is rarely affected, unless you do not take these assignments seriously (in which case you fail badly).

Promoting self-awareness and ownership. This is a hard one. One I am still struggling with. What I noticed in the way we teach, is that we give too little responsibility to students to “own” the burden of becoming knowledgeable in a given topic. While structure, syllabi, grading, etc., have their role, they also have a dark side: they allow students to just blindly follow whatever they are asked to do as automaton; without self-reflection and criticism. This is not a good model. Life out of school does not work this way. It does not matter how good your grades are, what really matters is learning about how to be responsible for your own education and empowerment. In the real world, nobody is feeding you with a spoon, you have to figure things out on your own (jobs where this happens tend to be crap by the way) and this is a way bigger lesson than actually learning the very content of the course.

So now … How do we encourage self-awareness and ownership? It’s hard. And I can’t say I have figured it all out. But I am carefully inserting little exercises and questions to encourage students to reflect more on what they are learning an why. For instance I would ask in the reading response things like: “why do you think this is important?” or “how are you going to apply this in the future?” or “how does this fit in your current and future knowledge?“. This is an area where I want to learn more. For now, I am just moving baby steps. If you have some ideas to share with me, I would be more than happy to learn!

Putting even more emphasis on “vis basics” and UI design. As a visualization designer and researcher, what I have learned throughout the years is that: (1) it is very rare for an “unconventional” chart to beat a conventional one; (2) it’s much more effective to learn how to use (and tweak) a few basic charts, then to explore a very large set of new fancy solutions as a first step; (3) you always need to start from the basics to realize they don’t work, and then change it. With this in mind, I am putting even more emphasis than usual in teaching students how to use well the most basic charts that cover fundamental combinations of 2 or 3 attributes (most charts out there don’t go past beyond 3).

Other than basic charts such as bar charts, heat maps, line charts etc., I’ll spend some time on maps because they are pervasive and are used in virtually every single data visualization job out there. Whereas I am growing way more doubtful about networks and trees. The heretic side of me is willing to heavily reduce the amount of time I’ll spend on them or even cut them out completely. I know many will harshly protest about this, but that’s the way I am thinking right now. Apologies.

A second aspect of my course is my focus on “analytical interfaces“: I teach students how to build interactive visual applications to help people make sense of data. I am teaching CSE students and I want them to acquire a special skill very few people have.

What I noticed in past editions of my course however is that students inevitably end up in a situation where they know how to design a single good chart/view, but have no idea whatsoever how to tie multiple charts and interactive components together in one complex UI. When you think about it, it’s astounding how little information and guidance exists out there on how to do this properly for analytical interfaces! This semester therefore I’ll put even more emphasis on this aspect and I’ll design new material to address this gap.

Asynchronous real-time communication tools to use in class. I started using Slack to communicate with my students inside and outside the classroom. I was fed up with the jurassic tools my university provides to communicate with students and I felt I needed a more direct connection. Slack is just perfect. Now I have a specific team for the course and receiving messages from students does not clutter up my (sacred) email inbox anymore.

But the most surprising advantage of Slack is that I can use it to communicate with my students in class! I know … It looks counterintuitive. But the thing is that in class, especially now that everyone is very active, there is often the need for me to: (1) send a link or document of some sort to everyone and (2) ask students to provide information to me asynchronously but in real-time. This second use is particularly useful. I can for instance ask a question and tell students to write an answer in Slack. This way they have time to work on it and I have time to process the results and produce feedback of some sort while they are working.

I am doing something similar with Google Docs: I assign an exercise to groups of students and ask them to produce the results in a Google Doc. As they do that, I can monitor what they are doing and intervene when necessary. For instance, I can identify issues with one group and help them directly, or sometime I can simply write a comment in the document and they’ll see it coming from me. I will experiment much more with these tools. So far, my experience is extremely positive.

Book suggestion. There is much more I have learned about new/better ways of teaching over the summer. I could go on forever. I just want to conclude suggesting an amazing book I read which inspired many of the changes I am making in the course. The book is “McKeachie’s Teaching Tips“. There are many good books around, but this one covers a lot in a small space. It’s a fantastic starting point to get the basics on a given aspect related to teaching. It provides an overview and lots of references you can then use to dig deeper if you want. It’s highly recommended.

This concludes my first entry for this semester’s course diary. I’ll write again as soon as I have more to say.

Hope you’ll enjoy it!

Paper: How Deceptive Are Deceptive Visualizations?

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We all know by now that visualization, thanks to its amazing communication powers, can be used to communicate effectively and persuasively massages that stick into people’s mind. This same power, however, can also be used to mislead and misinform people very effectively! When techniques like non-zero baselines, scaling by area (quadratic change to represent linear changes), bad color maps, etc., are used, it is very easy to communicate the wrong message to your readers (being that done on purpose or for lack of better knowledge). But, how easy is it?

Continue reading

What do we do this VIS thing for? Towards a data visualization ethos.

I often find myself asking: “What do we do this Data Visualization thing for?”. Of course I do it mostly because it’s fun, and I bet it’s the same for you. Yet, is there a way we can find some deeper meaning in it? Are there some higher level purposes we can identify? Meaning often comes in relation to impact one can have on other people’s lives, so here is a tentative list off the top of my head of how vis can impact people’s lives (feel free to add yours in the comments below). Continue reading

Book: Statistics as Principled Argument

stats-principledI just started reading Statistics as Principled Argument and I could not resist to start writing something about it because, simply stated, it’s awesome.

The reason why I am so excited is because this is probably the first stats book I found that focusses exclusively on the narrative and rhetorical side of statistics.

Abelson makes explicit what most people don’t seem to see, or be willing to admit: it does not matter how rigorous your data collection and analysis is (and by the way it’s very hard to be rigorous in the first place), every conclusion you draw out of data is is full of rhetoric. Continue reading

How do you evaluate communication-oriented visualization?

Had a fantastic visit at ProPublica yesterday (thanks Alberto for inviting me and Scott for having me, you have an awesome team!) and we discussed about lots of interesting things at the intersection of data visualization, literacy, statistics, journalism, etc. But there is one thing that really caught my attention. Lena very patiently (thanks Lena!) showed me some of the nice visualizations she created and then asked:

How do you evaluate visualization?

How do you know if you have done things right?

Heck! This is the kind of question I should be able to answer. I did have some suggestion for her, yet I realize there are no established methodologies. This comes as a bit of a surprise to me as I have been organizing the BELIV Workshop on Visualization Evaluation for a long time and I have been running user studies myself for quite some time now. Continue reading